Fares Qeadan

Objective: To develop and validate a weighting framework to improve representativeness of Oracle Health Real-World Data (OHRWD). Materials and Methods: We conducted cross-sectional analyses of OHRWD encounters in 2019 and 2022. The primary method (M1) applied design weights based on American Hospital Association (AHA) hospital encounter counts to balance OHRWD encounters across strata of US region, hospital system, bed size, and encounter type (inpatient, emergency department, ambulatory surgery). Comparative methods (M2-M5) used unified structural weights, encounter-type multipliers, iterative proportional fitting, and demographic post-stratification. Weighted OHRWD estimates were validated against the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS), Nationwide Emergency Department Sample (NEDS), and Nationwide Ambulatory Surgery Sample (NASS) for demographics, conditions (low back pain, opioid use disorder (OUD), hypertension, diabetes), and procedures (colonoscopy, appendectomy). Equivalence was assessed using two one-sided tests with ±20% margins; standardized differences were summarized using Cohen’s d and h. Results: M1 produced close alignment between OHRWD and HCUP for age, sex, region, and most race/ethnicity groups, with negligible effect sizes. Low back pain, OUD, and appendectomy prevalences closely matched HCUP benchmarks, whereas hypertension and, in some settings, diabetes and Hispanic ethnicity showed larger deviations. Alternative methods (M2-M5) yielded mixed performance and did not consistently outperform M1. Discussion: Encounter-type-specific, structurally informed weighting improved alignment with HCUP national benchmarks, highlighting domains, such as hypertension and race/ethnicity estimates, that require additional calibration. Conclusion: AHA-based, encounter-type-stratified weighting enables OHRWD to better approximate encounter patterns and could support epidemiologic and health services research using large EHR datasets.

Aim: Migraine is prevalent in college populations and commonly co-occurs with mental health conditions. This study assessed whether self-reported migraine-related healthcare interaction status was associated with substance use indicators among U.S. college students engaged in migraine-related care and the extent to which measured mental health diagnoses statistically explained these associations. Methods: We analyzed pooled repeated cross-sectional data from the American College Health Association-National College Health Assessment (ACHA-NCHA III/IIIb; Fall 2019-Spring 2025). The analytic sample included 37,982 students reporting a migraine-related appointment/discussion within the past 12 months. The exposure was self-reported migraine-related healthcare interaction status, categorized as received treatment, appointment/discussion without treatment, or told no treatment was necessary. Outcomes were binary indicators of past-30-day alcohol use and cannabis use (including medical and non-medical use), past-2-week binge drinking, past-3-month non-medical prescription drug misuse and other illegal drug use, polysubstance use, and alcohol-prescription drug co-use (co-reporting). Associations were estimated using multivariable logistic regression. Exploratory four-way decomposition was used to assess the extent to which depression, anxiety, or posttraumatic stress disorder (PTSD) statistically explained selected associations. Results: Most students reported receiving migraine treatment (76.9%); 15.3% reported an appointment/discussion without treatment and 7.8% reported being told no treatment was necessary. Compared with students who received treatment, those reporting an appointment/discussion without treatment had higher adjusted odds of alcohol use (adjusted odds ratio [aOR]:1.088, 95% confidence interval [CI]: 1.022–1.159), binge drinking (aOR: 1.186, 95% CI: 1.104–1.275), cannabis use (aOR: 1.222, 95% CI: 1.142–1.309), other illegal drug use (aOR: 1.227, 95% CI: 1.074–1.403), and polysubstance use (aOR: 1.213, 95% CI: 1.131–1.300). Students told no treatment was necessary showed a broadly similar pattern across these outcomes. Co-use of alcohol with prescription drugs was not statistically significant after adjustment. Exploratory decomposition suggested that measured depression, anxiety, or PTSD accounted for a modest proportion of cannabis and polysubstance associations; other estimates, particularly interaction-related components for other illegal drug use, were unstable and interpreted cautiously. Conclusions: Among U.S. college students engaged in migraine-related care, self-reported healthcare interaction categories indicating an appointment/discussion without treatment or being told no treatment was necessary were associated with modestly higher odds of several substance use indicators. These categories should be interpreted as healthcare interaction categories rather than measures of treatment adequacy. Findings support hypothesis-generating attention to integrated migraine-related care and substance-use screening in college health settings.

Co-occurring opioid and alcohol use disorders (OUD + AUD) are associated with morbidity, yet medications for these disorders remain underused. Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) receptor agonists (RA), prescribed for metabolic disease, may influence substance-related outcomes. This study aims to estimate associations between GIP/GLP-1 RA prescriptions, first-line AUD and OUD medications and several adverse healthcare outcomes among those with co-occurring OUD + AUD. We conducted a retrospective cohort study using de-identified electronic health records from Oracle Health Real-World Data (149 U.S. health systems; 2014-2024). Among 107,217 patients aged ≥12 years with dual OUD + AUD diagnoses, time-varying exposures included medications for OUD (MOUD), medications for AUD (MAUD), and GIP/GLP-1 RA prescriptions. Outcomes over 2 years included overdoses and intoxications, SUD-related hospitalizations, detoxification, positive drug screens, mental health events, incident liver conditions, and liver biomarkers. Marginal structural survival models with stabilized inverse probability of treatment weighting estimated adjusted hazard ratios (aHRs) overall and stratified by MOUD/MAUD status, type 2 diabetes (T2DM), and obesity. GIP/GLP-1 RA prescriptions are associated with lower risk of all-drug overdose among patients not receiving MOUD/MAUD (aHR 0.61; 95% CI 0.32-0.98). In stratified analyses, lower overdose risk is observed among patients with T2DM receiving MAUD only (aHR 0.15; 95% CI 0.01-0.41) and among those receiving both MOUD and MAUD (T2DM: aHR 0.46; 95% CI 0.24-0.87; obesity: aHR 0.19; 95% CI 0.04-0.97). Protective associations for SUD-related hospitalization are observed across most strata. In this large EHR cohort, GIP/GLP-1 RA prescriptions are linked to reduced overdose and hospitalization risk among patients with co-occurring OUD + AUD. Findings are exploratory and limited by small subgroup sizes; prospective studies are needed to confirm benefit and evaluate safety.